{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/arya/.local/lib/python3.6/site-packages/sklearn/utils/deprecation.py:144: FutureWarning: The sklearn.ensemble.forest module is  deprecated in version 0.22 and will be removed in version 0.24. The corresponding classes / functions should instead be imported from sklearn.ensemble. Anything that cannot be imported from sklearn.ensemble is now part of the private API.\n",
      "  warnings.warn(message, FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.metrics import log_loss\n",
    "import lightgbm as lgb\n",
    "from sklearn.model_selection import StratifiedKFold,KFold,GridSearchCV,GroupKFold,train_test_split\n",
    "from rfpimp import *\n",
    "from xgboost import XGBClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train = pd.read_csv('Train.csv')\n",
    "df_test = pd.read_csv('Test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_sub = pd.read_excel('Sample_Submission.xlsx')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets see some stats of training and test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1    0.682790\n",
      "2    0.287964\n",
      "0    0.029246\n",
      "Name: Gender, dtype: float64\n",
      "0    0.87964\n",
      "1    0.12036\n",
      "Name: Chain_smoker, dtype: float64\n",
      "1    0.83802\n",
      "0    0.16198\n",
      "Name: Consumes_other_tobacco_products, dtype: float64\n",
      "0    0.913386\n",
      "1    0.086614\n",
      "Name: HighBP, dtype: float64\n",
      "1    0.91901\n",
      "0    0.08099\n",
      "Name: Obese, dtype: float64\n",
      "0    0.944882\n",
      "1    0.055118\n",
      "Name: Diabetes, dtype: float64\n",
      "0    0.957255\n",
      "1    0.042745\n",
      "Name: Metabolic_syndrome, dtype: float64\n",
      "0    0.917885\n",
      "1    0.082115\n",
      "Name: Use_of_stimulant_drugs, dtype: float64\n",
      "1    0.925759\n",
      "0    0.074241\n",
      "Name: Family_history, dtype: float64\n",
      "0    0.982002\n",
      "1    0.017998\n",
      "Name: History_of_preeclampsia, dtype: float64\n",
      "0    0.978628\n",
      "1    0.021372\n",
      "Name: CABG_history, dtype: float64\n",
      "0    0.967379\n",
      "1    0.032621\n",
      "Name: Respiratory_illness, dtype: float64\n",
      "no     0.786277\n",
      "yes    0.213723\n",
      "Name: UnderRisk, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "for x in df_train.columns:\n",
    "    print(df_train[x].value_counts(normalize=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1    0.651832\n",
      "2    0.321990\n",
      "0    0.026178\n",
      "Name: Gender, dtype: float64\n",
      "0    0.879581\n",
      "1    0.120419\n",
      "Name: Chain_smoker, dtype: float64\n",
      "1    0.793194\n",
      "0    0.206806\n",
      "Name: Consumes_other_tobacco_products, dtype: float64\n",
      "0    0.945026\n",
      "1    0.054974\n",
      "Name: HighBP, dtype: float64\n",
      "1    0.89267\n",
      "0    0.10733\n",
      "Name: Obese, dtype: float64\n",
      "0    0.947644\n",
      "1    0.052356\n",
      "Name: Diabetes, dtype: float64\n",
      "0    0.939791\n",
      "1    0.060209\n",
      "Name: Metabolic_syndrome, dtype: float64\n",
      "0    0.89267\n",
      "1    0.10733\n",
      "Name: Use_of_stimulant_drugs, dtype: float64\n",
      "1    0.924084\n",
      "0    0.075916\n",
      "Name: Family_history, dtype: float64\n",
      "0    0.994764\n",
      "1    0.005236\n",
      "Name: History_of_preeclampsia, dtype: float64\n",
      "0    0.979058\n",
      "1    0.020942\n",
      "Name: CABG_history, dtype: float64\n",
      "0    0.968586\n",
      "1    0.031414\n",
      "Name: Respiratory_illness, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "for x in df_test.columns:\n",
    "    print(df_test[x].value_counts(normalize=True))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looks the same to me not much of difference, probably a straifiedkfold should be enough"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "no     0.846154\n",
       "yes    0.153846\n",
       "Name: UnderRisk, dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train[df_train['Gender'] == 0]['UnderRisk'].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "no     0.759473\n",
       "yes    0.240527\n",
       "Name: UnderRisk, dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train[df_train['Gender'] == 1]['UnderRisk'].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "no     0.84375\n",
       "yes    0.15625\n",
       "Name: UnderRisk, dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train[df_train['Gender'] == 2]['UnderRisk'].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similary as above i tried to see of there are some definite relationships of the features with the target so that i can just create a rule but there didn't seem to be any."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "risk = {\n",
    "    'no':0,\n",
    "    'yes':1\n",
    "}\n",
    "df_train['UnderRisk'] = df_train['UnderRisk'].map(risk)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7ff00c5cde10>,\n",
       " <matplotlib.lines.Line2D at 0x7fefb638d0f0>,\n",
       " <matplotlib.lines.Line2D at 0x7fefb638dc88>,\n",
       " <matplotlib.lines.Line2D at 0x7fefbcea8630>,\n",
       " <matplotlib.lines.Line2D at 0x7fefbce70a20>,\n",
       " <matplotlib.lines.Line2D at 0x7fefbce70b38>,\n",
       " <matplotlib.lines.Line2D at 0x7fefb6b8c518>,\n",
       " <matplotlib.lines.Line2D at 0x7fefb6b8cc88>,\n",
       " <matplotlib.lines.Line2D at 0x7fefb6b8c898>,\n",
       " <matplotlib.lines.Line2D at 0x7fefb6b8c5c0>,\n",
       " <matplotlib.lines.Line2D at 0x7fefb638dda0>,\n",
       " <matplotlib.lines.Line2D at 0x7fefb6b8ca58>,\n",
       " <matplotlib.lines.Line2D at 0x7fefb6b8cc50>]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(df_train,' .')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is just my way of seeing how the data was separated , like by what column or by what value row wise, x is row indices and y is a column seems like it was separated via gender."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fefb6188390>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x=df_train.values[:,6],y=range(len(df_train)),c=df_train['UnderRisk'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Just trying to see if there are variables that can get me good class separation but there isnt any i mean, thats probably the red flag that this data doesnt need much parameter tuning and feature engineering is just impossible here and also i have to remove noise because that will actually make my model unstable ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "best = ['Family_history',\n",
    " 'HighBP',\n",
    " 'Chain_smoker',\n",
    " 'Metabolic_syndrome',\n",
    " 'Gender',\n",
    " 'Obese']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using Feature Importance i got the best features as above"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train,X_valid,y_train,y_valid = train_test_split(df_train.drop('UnderRisk',axis=1),df_train['UnderRisk'],stratify=df_train['UnderRisk'],random_state=22)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fold 0\n",
      "[1]\tvalid_0's binary_logloss: 0.521521\n",
      "Training until validation scores don't improve for 100 rounds\n",
      "[2]\tvalid_0's binary_logloss: 0.520671\n",
      "[3]\tvalid_0's binary_logloss: 0.519836\n",
      "[4]\tvalid_0's binary_logloss: 0.519014\n",
      "[5]\tvalid_0's binary_logloss: 0.518206\n",
      "[6]\tvalid_0's binary_logloss: 0.517411\n",
      "[7]\tvalid_0's binary_logloss: 0.51663\n",
      "[8]\tvalid_0's binary_logloss: 0.51586\n",
      "[9]\tvalid_0's binary_logloss: 0.515104\n",
      "[10]\tvalid_0's binary_logloss: 0.514359\n",
      "[11]\tvalid_0's binary_logloss: 0.513626\n",
      "[12]\tvalid_0's binary_logloss: 0.512905\n",
      "[13]\tvalid_0's binary_logloss: 0.512195\n",
      "[14]\tvalid_0's binary_logloss: 0.511496\n",
      "[15]\tvalid_0's binary_logloss: 0.510808\n",
      "[16]\tvalid_0's binary_logloss: 0.510131\n",
      "[17]\tvalid_0's binary_logloss: 0.509464\n",
      "[18]\tvalid_0's binary_logloss: 0.508807\n",
      "[19]\tvalid_0's binary_logloss: 0.508161\n",
      "[20]\tvalid_0's binary_logloss: 0.507524\n",
      "[21]\tvalid_0's binary_logloss: 0.506897\n",
      "[22]\tvalid_0's binary_logloss: 0.506279\n",
      "[23]\tvalid_0's binary_logloss: 0.505671\n",
      "[24]\tvalid_0's binary_logloss: 0.505071\n",
      "[25]\tvalid_0's binary_logloss: 0.504481\n",
      "[26]\tvalid_0's binary_logloss: 0.503899\n",
      "[27]\tvalid_0's binary_logloss: 0.503326\n",
      "[28]\tvalid_0's binary_logloss: 0.502761\n",
      "[29]\tvalid_0's binary_logloss: 0.502205\n",
      "[30]\tvalid_0's binary_logloss: 0.501656\n",
      "[31]\tvalid_0's binary_logloss: 0.501116\n",
      "[32]\tvalid_0's binary_logloss: 0.500583\n",
      "[33]\tvalid_0's binary_logloss: 0.500058\n",
      "[34]\tvalid_0's binary_logloss: 0.49954\n",
      "[35]\tvalid_0's binary_logloss: 0.49903\n",
      "[36]\tvalid_0's binary_logloss: 0.498527\n",
      "[37]\tvalid_0's binary_logloss: 0.498032\n",
      "[38]\tvalid_0's binary_logloss: 0.497543\n",
      "[39]\tvalid_0's binary_logloss: 0.497061\n",
      "[40]\tvalid_0's binary_logloss: 0.496586\n",
      "[41]\tvalid_0's binary_logloss: 0.496117\n",
      "[42]\tvalid_0's binary_logloss: 0.495655\n",
      "[43]\tvalid_0's binary_logloss: 0.495199\n",
      "[44]\tvalid_0's binary_logloss: 0.49475\n",
      "[45]\tvalid_0's binary_logloss: 0.494307\n",
      "[46]\tvalid_0's binary_logloss: 0.49387\n",
      "[47]\tvalid_0's binary_logloss: 0.493439\n",
      "[48]\tvalid_0's binary_logloss: 0.493014\n",
      "[49]\tvalid_0's binary_logloss: 0.492594\n",
      "[50]\tvalid_0's binary_logloss: 0.492185\n",
      "[51]\tvalid_0's binary_logloss: 0.491782\n",
      "[52]\tvalid_0's binary_logloss: 0.491329\n",
      "[53]\tvalid_0's binary_logloss: 0.490936\n",
      "[54]\tvalid_0's binary_logloss: 0.490495\n",
      "[55]\tvalid_0's binary_logloss: 0.490061\n",
      "[56]\tvalid_0's binary_logloss: 0.489633\n",
      "[57]\tvalid_0's binary_logloss: 0.489212\n",
      "[58]\tvalid_0's binary_logloss: 0.488797\n",
      "[59]\tvalid_0's binary_logloss: 0.488434\n",
      "[60]\tvalid_0's binary_logloss: 0.48803\n",
      "[61]\tvalid_0's binary_logloss: 0.487632\n",
      "[62]\tvalid_0's binary_logloss: 0.48724\n",
      "[63]\tvalid_0's binary_logloss: 0.486853\n",
      "[64]\tvalid_0's binary_logloss: 0.486472\n",
      "[65]\tvalid_0's binary_logloss: 0.486138\n",
      "[66]\tvalid_0's binary_logloss: 0.485767\n",
      "[67]\tvalid_0's binary_logloss: 0.485401\n",
      "[68]\tvalid_0's binary_logloss: 0.485041\n",
      "[69]\tvalid_0's binary_logloss: 0.484686\n",
      "[70]\tvalid_0's binary_logloss: 0.484336\n",
      "[71]\tvalid_0's binary_logloss: 0.484026\n",
      "[72]\tvalid_0's binary_logloss: 0.483685\n",
      "[73]\tvalid_0's binary_logloss: 0.483349\n",
      "[74]\tvalid_0's binary_logloss: 0.483017\n",
      "[75]\tvalid_0's binary_logloss: 0.482691\n",
      "[76]\tvalid_0's binary_logloss: 0.482368\n",
      "[77]\tvalid_0's binary_logloss: 0.482081\n",
      "[78]\tvalid_0's binary_logloss: 0.481767\n",
      "[79]\tvalid_0's binary_logloss: 0.481458\n",
      "[80]\tvalid_0's binary_logloss: 0.481153\n",
      "[81]\tvalid_0's binary_logloss: 0.480852\n",
      "[82]\tvalid_0's binary_logloss: 0.480555\n",
      "[83]\tvalid_0's binary_logloss: 0.480288\n",
      "[84]\tvalid_0's binary_logloss: 0.479999\n",
      "[85]\tvalid_0's binary_logloss: 0.479713\n",
      "[86]\tvalid_0's binary_logloss: 0.479432\n",
      "[87]\tvalid_0's binary_logloss: 0.479154\n",
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      "Early stopping, best iteration is:\n",
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      "Fold 1\n",
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      "Early stopping, best iteration is:\n",
      "[136]\tvalid_0's binary_logloss: 0.496011\n",
      "Fold 2\n",
      "[1]\tvalid_0's binary_logloss: 0.514282\n",
      "Training until validation scores don't improve for 100 rounds\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fold 4\n",
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      "[718]\tvalid_0's binary_logloss: 0.45188\n",
      "[719]\tvalid_0's binary_logloss: 0.451879\n",
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      "[722]\tvalid_0's binary_logloss: 0.45187\n",
      "[723]\tvalid_0's binary_logloss: 0.451873\n",
      "[724]\tvalid_0's binary_logloss: 0.451876\n",
      "[725]\tvalid_0's binary_logloss: 0.451878\n",
      "[726]\tvalid_0's binary_logloss: 0.451881\n",
      "[727]\tvalid_0's binary_logloss: 0.451884\n",
      "[728]\tvalid_0's binary_logloss: 0.451887\n",
      "[729]\tvalid_0's binary_logloss: 0.451885\n",
      "[730]\tvalid_0's binary_logloss: 0.451884\n",
      "Early stopping, best iteration is:\n",
      "[630]\tvalid_0's binary_logloss: 0.451851\n",
      "0.4749724980884557\n"
     ]
    }
   ],
   "source": [
    "folds = StratifiedKFold(n_splits=5, shuffle=True, random_state=22)\n",
    "predictions = np.zeros((len(X_valid), 2))\n",
    "oof_preds = np.zeros((len(df_test), 2))\n",
    "feature_importance_df = pd.DataFrame()\n",
    "final_preds = []\n",
    "# random_state = [77,89,22,1007,1997,1890,2000,2020,8989,786,787,1999992,2021,7654]\n",
    "for fold_, (trn_idx, val_idx) in enumerate(folds.split(X_train.values, y_train.values)):\n",
    "        print(\"Fold {}\".format(fold_))\n",
    "        X_trn,y_trn = X_train[best].iloc[trn_idx],y_train.iloc[trn_idx]\n",
    "        X_val,y_val = X_train[best].iloc[val_idx],y_train.iloc[val_idx]\n",
    "        clf  = lgb.LGBMClassifier(random_state=22,learning_rate=0.01,n_estimators=4000)\n",
    "        clf.fit(X_trn, y_trn, eval_metric=\"logloss\", eval_set=[(X_val,y_val)], verbose=True,early_stopping_rounds=100)\n",
    "        final_preds.append(log_loss(y_pred=clf.predict_proba(X_val),y_true=y_val))\n",
    "        predictions += clf.predict_proba(X_valid[best])\n",
    "#         oof_preds += clf_lgb.predict_proba(df_test[k])\n",
    "#         counter = counter + 1\n",
    "# oof_preds = oof_preds/counter\n",
    "print(sum(final_preds)/5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.4813758395132681\n"
     ]
    }
   ],
   "source": [
    "print(log_loss(y_pred=predictions/5,y_true=y_valid))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This Gave me the best Leaderboard Score , i also tried training the model and trying to average out the results using different random states but it just didnt really work. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_sub['no'] = y_preds[:,0]\n",
    "df_sub['yes'] = y_preds[:,1]\n",
    "df_sub.to_excel('Blended.xlsx',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
